Latest / The Adults In The AI Room / Jim Iyoob at the intersection of AI and CX - Episode #16

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Jim Iyoob at the intersection of AI and CX - Episode #16 · The Adults In The AI Room

0:04
SPEAKER_00: Welcome.
0:04
SPEAKER_00: You're listening to the Early AI Podcast.
0:07
SPEAKER_00: Here we talk with thought leaders and practitioners on what's possible and practical with AI in the enterprise as we move from the current state to a future of cognitive assistance and embedded AI in all aspects of our personal and professional lives.
0:22
SPEAKER_00: Thanks so much for listening.
0:23
SPEAKER_00: If this is your first time, we invite you to subscribe wherever you listen to podcasts.
0:28
SPEAKER_00: And now here are your hosts, Seth Early and Chris Featherstone.
0:36
SPEAKER_02: Welcome to today's podcast.
0:39
SPEAKER_02: I'm Seth Early.
0:41
SPEAKER_05: I'm Chris Featherstone.
0:42
SPEAKER_05: It's good to be with you, man.
0:43
SPEAKER_05: It seems like it's been forever, huh?
0:45
SPEAKER_02: I know, I know.
0:46
SPEAKER_02: It's you've been busy.
0:47
SPEAKER_02: I've been busy.
0:48
SPEAKER_02: I was just mentioning we had one of our knowledge salons on Monday, which was fun.
0:52
SPEAKER_02: It was relatively small, but it's one of the things that uh Tom Davenport wrote about in his uh work to my book.
0:59
SPEAKER_02: He said, I think the four things could set the early knowledge salons uh around the fire fire pit at his home.
1:04
SPEAKER_02: Uh a crazy fast uh uh uh in uh uh uh power racing catamaran on which he risked his safety and then knowledge management and AI.
1:14
SPEAKER_02: But uh took some people out on the on the uh the Thundercat, and uh that was a lot of fun as well.
1:19
SPEAKER_02: Not on not at the knowledge salon, but I took some of my folks and traveled in front of us.
1:23
SPEAKER_05: I assume a knowledge salon is like a little uh fireside chat type of a scenario, just a little quaint.
1:28
SPEAKER_05: Yeah, you know.
1:28
SPEAKER_02: So we basically break up into groups, I have topics and little table times.
1:32
SPEAKER_02: We're actually going to be doing one in September, it's gonna be a bigger one.
1:36
SPEAKER_02: So I'll let people know about that.
1:38
SPEAKER_02: And uh maybe if some of your travels bring you to Boston, you could join us.
1:41
SPEAKER_02: But uh they've been successful, they've been a lot of fun.
1:44
SPEAKER_02: Of course, we had hiatus during the pandemic, but uh looking forward to starting them up again.
1:49
SPEAKER_02: So um, well, let's jump in.
1:51
SPEAKER_02: Our guest today is Chief Customer Officer for eTech Global Services.
1:56
SPEAKER_02: His responsibility spans across all of eTech's existing lines of business.
2:00
SPEAKER_02: He is a 25-year contact center veteran with experience in inbound, outbound chat operations and social media management around the world.
2:10
SPEAKER_02: His work has been featured in numerous publications, including CIO Review, Contact Center Pipeline, Connections magazine, and was and he was also listed as one of the top 20 influential people to follow on Twitter.
2:22
SPEAKER_02: I want it now.
2:24
SPEAKER_02: And he was just inducted into the 2022 uh Hall of Fame for uh CX, for customer experience.
2:33
SPEAKER_02: Please welcome our guest, Jim Ayu.
2:35
SPEAKER_01: Oh, thank you so much, Seth and Chris.
2:37
SPEAKER_01: It's a pleasure to meet you guys.
2:38
SPEAKER_01: And I'm just uh humbled to be here and I'm looking forward to today's podcast.
2:42
SPEAKER_05: This is this is gonna be a fun one because you know, besides you know my day job, we geek out on a lot of this kind of stuff.
2:48
SPEAKER_05: And and what's what's fun too, Jim, is that generally I'm always in awe of the circles that Seth gets, you know, runs in with these folks from MIT and from Stanford and from all these you know unbelievable schools and just knowledge and think tank type of you know world.
3:04
SPEAKER_05: And and I'm always asking them, well, let's talk about brass tax.
3:08
SPEAKER_05: Like, how does that thought algorithm or outcome actually really drive a business outcome like customer experience?
3:17
SPEAKER_05: And um, and I think they get sick of me asking those questions, but now you're the topic of the hour, so this is gonna be super fun for me.
3:25
SPEAKER_01: I I think we're both gonna have some fun because hopefully you'll find for me is like I'm very passionate about the experience, and uh I think that's really what brings me into some of those accolades I've gotten uh over the years, is because at the end of the day, it's about that experience.
3:44
SPEAKER_01: And if you're not doing something about it in a global marketplace, Chris, you could buy it anywhere.
3:50
SPEAKER_05: Yep, exactly right.
3:51
SPEAKER_05: So you're you're in Texas, right?
3:53
SPEAKER_05: Seth's in Boston.
3:54
SPEAKER_05: Seth just had he's not he doesn't follow MB you know basketball, but he had a catastrophic loss last night against Golden State.
4:03
SPEAKER_02: I'm glad I'm glad that I don't follow and I'm not passionate about catastrophic to me.
4:11
SPEAKER_05: But uh yeah, so how's it going on down in Texas?
4:13
SPEAKER_05: Because it seems to be two things are happening in the world today in the US, you know, you know, in terms of besides all the political pandemic stuff that's going on.
4:22
SPEAKER_05: We've either got half the country is in a heat wave or half the country is flooding.
4:26
SPEAKER_05: And I'm in the portion of the country where it's flooding.
4:29
SPEAKER_05: And you guys, I think, are in the country in part of the country where it's there's no water and it's uh you know, drought and you're in a heat wave, right?
4:35
SPEAKER_05: Exactly.
4:35
SPEAKER_01: I it's it's 104 today.
4:38
SPEAKER_01: I'm looking out my front window, my grass is dying, my wife is yelling at me because I keep putting the water on, even though we're in a drought.
4:45
SPEAKER_02: So so Jim, give us a sense of tell us about your world and and tell us about the CX Hall of Fame.
4:52
SPEAKER_02: So, you know, give us your story about how you kind of got into the space and what led you to uh where you are.
4:59
SPEAKER_01: Yeah, yeah.
4:59
SPEAKER_01: So um the fortunate for me, I've had the same boss pretty much my whole life.
5:04
SPEAKER_01: Um, when I came out of college, I actually went to Dunham Bradstreet.
5:09
SPEAKER_01: And I and I remember I met this guy by the name of Matt Rocco, who's e tech's president and CEO now.
5:14
SPEAKER_01: And I remember he walked, I was just an agent.
5:17
SPEAKER_01: So I started my career as an agent, just doing basic stuff that everybody's doing.
5:21
SPEAKER_01: And I remember he I walked into his office, you know, he brought me in and says, Hey, what do you want to do?
5:26
SPEAKER_01: And I looked at him and said, I want your job because he had this big Christie office.
5:30
SPEAKER_01: And for whatever reason, uh, we look back 30 years.
5:34
SPEAKER_01: He didn't fire me, but for whatever reason, he took me under his wing and he really became my unofficial mentor.
5:41
SPEAKER_01: So nine months working for him, I've been promoted, I've gotten into working some of the bigger parts of Dunham Brad Street.
5:47
SPEAKER_01: I no longer work for him, but throughout those years I was at Dunham Brad Street, he's always been my mentor.
5:53
SPEAKER_01: And we went out for dinner one night.
5:55
SPEAKER_01: He says, Hey, uh, we're moving to Nacadoches, Texas.
5:58
SPEAKER_01: I said, Where is that?
6:00
SPEAKER_01: He says, You'll find out.
6:02
SPEAKER_01: So he recruited me to come into at the time was called Barry Direct.
6:08
SPEAKER_01: It was a one little call center, 400 seats, one customer.
6:13
SPEAKER_01: And uh he hired me as an account manager.
6:16
SPEAKER_01: So, like that was my first role.
6:17
SPEAKER_01: I was managing one account.
6:20
SPEAKER_01: Yeah, yeah, one account, and I managed the coaches and the clients.
6:24
SPEAKER_01: Uh, two years after we got here, they divested of all the assets.
6:28
SPEAKER_01: Bell South did, and uh, he took the company private.
6:31
SPEAKER_01: So I will fast forward you till today.
6:34
SPEAKER_01: ETEC operates, we have 10 call centers in three countries.
6:39
SPEAKER_01: We've done this with no mergers, no acquisitions, no venture capitalist, and we have about 3,600 employees globally today.
6:48
SPEAKER_01: Obviously, uh, I was part of that whole journey.
6:51
SPEAKER_01: Um, Matt Rocco has done a great job with e tech instilling servant leadership, which is how you're tying back into the CX.
6:59
SPEAKER_01: So when you instill people first culture, technology is an enabler, we get it.
7:04
SPEAKER_01: But when you talk about people being first and a people first culture, and you get people will be actually happy to work for you.
7:12
SPEAKER_01: And at the end of the day, everything I learned as a as an agent, as a coach, as a as a frontline leader, all those things, I said, I have to give the best experience, not only to our employees, but to that customer.
7:26
SPEAKER_01: And then over time, what I've seen is some of these companies come in, they have three-piece suits, they have all this great knowledge, they do everything, they have beautiful PowerPoints.
7:36
SPEAKER_01: The end of the day, they're selling these technologies and tech stacks that at the end of the day, that's not the holy grail.
7:42
SPEAKER_01: And I think we're going to dig into it better.
7:43
SPEAKER_01: I think it's the people, and that's that whole culture that we're seeing now.
7:48
SPEAKER_01: Um, as far as the CX, I mean, I think the reason I've gotten accolades on that, I'm I'm I'm humbled by that because Seth, I'm nowhere in the circle that you're at.
7:58
SPEAKER_01: But one of my goals is throughout my career, if you can add value, right?
8:04
SPEAKER_01: If you can add value, you can be a great person.
8:08
SPEAKER_01: And when what I look at these customer experiences, I'm looking at it from the customer's lens, not from, you know, meaning the end customer, not the brand, you know, and I think that's kind of what helped me.
8:20
SPEAKER_01: And I've been educating in the 20 years of my plus of my career, I've always done education because there's so much information out there.
8:29
SPEAKER_01: How do you educate people?
8:30
SPEAKER_01: And Chris, what you're talking about with these tech stacks, how do you make it actionable?
8:34
SPEAKER_01: How do you get down to that level, right?
8:37
SPEAKER_01: And that's what I've always done.
8:38
SPEAKER_01: And last but not least, we're software agnostic.
8:42
SPEAKER_01: Because I don't believe, and Seth, you know this because I've talked about it all the time.
8:46
SPEAKER_01: There, everybody says, like, who is the best for speech?
8:48
SPEAKER_01: Who's the best?
8:49
SPEAKER_01: There is no best.
8:50
SPEAKER_01: First, you have to identify the opportunity that the customer's trying to solve for.
8:55
SPEAKER_01: Once you understand that, then and only then do you earn the right to give them a recommendation?
9:01
SPEAKER_05: Yeah, all you have to do, it's like throwing a snowball into hell.
9:04
SPEAKER_05: All you have to do is say, Oh, you want to know the best?
9:07
SPEAKER_05: Well, wait, you want to do Hindi, you know, English accents?
9:13
SPEAKER_05: Oh, that changes the game.
9:14
SPEAKER_05: You want to do, you know, something where you know it's an industry-specific compliance related type of thing.
9:20
SPEAKER_05: Oh, yeah, it changes the game.
9:21
SPEAKER_05: So you're level setting all the time, and I agree with you 100%.
9:25
SPEAKER_05: Yep.
9:25
SPEAKER_05: Um, it's amazing to me.
9:27
SPEAKER_05: And we'll I I want to double click or triple click really into some of these aspects and stuff centered around that the organizations, you know, it's it's funny that there's 60 to 160% attrition year or year with these call center environments and things like that.
9:43
SPEAKER_05: And yet they don't think about maybe or care about the psychology of what they're doing to their most their most valuable asset, which is their human capital.
9:53
SPEAKER_05: And they don't, they treat them, it's like that book um that that's uh that's that's circulating with the the the journalist that that went out and she actually she worked in an Amazon distribution center, she worked at McDonald's, and she worked in a in a concentrics call center.
10:09
SPEAKER_05: And it was called on the clock.
10:11
SPEAKER_05: And it's it actually makes you mad and empathetic towards her and what what organizations today do to these folks at an hourly rate and the pressure cooker they put them under and expect them to provide this unbelievable customer experience that drives loyalty and that drives like it's it's amazing that you know, think about the indifference.
10:39
SPEAKER_05: They're paid some of the worst, yet held to some of the highest standards of an organization.
10:45
SPEAKER_05: What what the hell are we missing?
10:47
SPEAKER_01: Yeah, I agree.
10:48
SPEAKER_01: And for the record, not everybody's like that, Chris.
10:50
SPEAKER_01: So it's totally a fine.
10:53
SPEAKER_02: How do you justify your your structure and and and your your care of your employees?
10:59
SPEAKER_01: Yeah, so if you think about analytics and these tech stacks, which we all have out there, and then how do you take that and intersect it with the culture, is what you're asking.
11:09
SPEAKER_01: Yeah.
11:10
SPEAKER_01: I mean, think about it.
11:12
SPEAKER_01: Technology is specifically performance management.
11:16
SPEAKER_01: If you use speech to text, it's an enabler, remember.
11:20
SPEAKER_01: It's just enables, it's a driver towards that positive culture, if deployed correctly.
11:28
SPEAKER_01: See, when you think of a positive culture, a couple things come to mind transparency, transparent performance evaluations, communicating key metrics consistently, which I think we miss sometimes.
11:43
SPEAKER_01: And the third one that I would talk about would be reduced agent effort.
11:49
SPEAKER_01: How hard is it for the agent to get the feedback and do their job?
11:54
SPEAKER_01: See, when you look at speech, is which I know we all talk about.
11:57
SPEAKER_01: When you look at speech, it actually hits a lot of those points.
12:01
SPEAKER_01: And there is no gray area, right?
12:03
SPEAKER_01: In in performance management, when you're listening to a lot of calls and a lot of things, you're not making biased decisions, right?
12:10
SPEAKER_01: Agents, supervisors, they have clear sight to what their KPIs are holistically, not on 10 calls or 12 calls, right?
12:20
SPEAKER_01: And when you think about an agent, and I've seen this because I used to be an agent, they interact with four to 10 different tool sets to get this data, right?
12:29
SPEAKER_01: So you want them on top of taking care of your customer, you want them to launch 16 different windows so they have opportunities to provide them, you know, automatically learn.
12:40
SPEAKER_01: And that's like these people that talk about the agent enablement where they're doing the they want to tell the agent in real time where the customer's getting frustrated.
12:49
SPEAKER_01: Really?
12:50
SPEAKER_01: On top of the 19 things they have open, you want to give them something else in their headset?
12:54
SPEAKER_01: Yeah.
12:54
SPEAKER_01: See, when you look at text analytics and like sentiment analysis, machine learning, you know, this domain expertise where I think is people are missing is the data scientist team that actually has the understanding of the product, the solution, and the CX indicators or behaviors we want.
13:17
SPEAKER_01: That's the leaders who are implementing these tech stacks.
13:20
SPEAKER_01: And no disrespect to the IT guys.
13:22
SPEAKER_01: The IT guy's sitting in the back, he's solving problems that we don't even need solved because he's probably never taken a day, a call day in his life.
13:30
SPEAKER_01: He probably has no idea what that agent is.
13:32
SPEAKER_01: And that's kind of what I look at from a how you intersect that tech and culture.
13:36
SPEAKER_01: Hopefully that answered you.
13:38
SPEAKER_05: Absolutely.
13:41
SPEAKER_05: And I I love the fact too that um, I mean, that's what I do on a day-to-day is build these tech stacks, right?
13:47
SPEAKER_05: For you know, people to enable.
13:49
SPEAKER_05: But I don't I don't approach it in the fact that a technology stack is going to solve your problems because it's just it's like you said, it's just an enabler.
13:57
SPEAKER_05: However, define irony that many of the tech stacks that we have today that facilitate this are completely operational, and that's it.
14:07
SPEAKER_05: They're operational focused, and generally it's a Herculean effort to get that operational effort to map to the business objectives of the organization, which that's what they use the human intelligence and human capital for is to try to bridge that.
14:23
SPEAKER_05: But a lot oftentimes, you know, to your point, we're holding them to these operational metrics.
14:28
SPEAKER_05: I hate average handle time, by the way.
14:29
SPEAKER_05: I hate MCs.
14:31
SPEAKER_04: Yep.
14:31
SPEAKER_05: They're all yes.
14:33
SPEAKER_05: Um in there, you know, they're one indicator of something, but you know, good data in, maybe good results out, bad data in, bad results.
14:43
SPEAKER_05: It's all you know, FIFO anyway.
14:44
SPEAKER_05: So when you think about that from um, you know, that perspective, you know, you speak to a lot of customers and a lot of organizations and stuff.
14:54
SPEAKER_05: What's the thing that you start with?
14:55
SPEAKER_05: How do you start with them?
14:56
SPEAKER_05: Because arguably they have these environments.
14:58
SPEAKER_05: I mean, these are 30-year-old technology stacks that we're talking about too.
15:01
SPEAKER_05: It's not like we're talking about something that's, you know, a digital native type of an approach, but we're trying to take these legacy things, provide, you know, layer them on digital natives and then tell them to be successful.
15:13
SPEAKER_05: So, how do you start with the organization?
15:15
SPEAKER_01: Yeah, so the first thing I tell people is domain expertise overshadows all the technical expertise you could have in this world, by the way.
15:21
SPEAKER_01: Unless you understand your customer, the tools at your disposal, they're not going to be able to help you.
15:27
SPEAKER_01: See, a lot of people don't look at data scientist teams.
15:31
SPEAKER_01: So at e tech, we have a data engineer, data analyst, and data scientist.
15:36
SPEAKER_01: There's a reason there's three different buckets, right?
15:39
SPEAKER_01: Because e-tech has industry-specific leaders in domain expertise, because they came from the call center that actually helps ensure when you implement whatever tech stack you're getting, you're going to become an expert not only on the product, right?
15:53
SPEAKER_01: Your product, whatever you're doing, also it brings you a fresh perspective, right?
15:58
SPEAKER_01: Without sharing trade secrets.
16:00
SPEAKER_01: You know, we're running three and a half million interactions a month through our through our various product um uh speech to text engines.
16:08
SPEAKER_01: And so now you have industry domain expertise.
16:11
SPEAKER_01: And you're absolutely right.
16:12
SPEAKER_01: When the guys are selling these tech stacks, they're selling this shiny new toy, right?
16:17
SPEAKER_01: They don't know how to use it.
16:18
SPEAKER_01: The sales guy doesn't know how to use it.
16:20
SPEAKER_01: He's just trying to get a sale.
16:21
SPEAKER_01: And I tell everybody this, and I'll be talking about this uh on Tuesday.
16:24
SPEAKER_01: So this I'll give you guys a preview.
16:27
SPEAKER_05: Hold on, hold on.
16:28
SPEAKER_05: So so tell tell them, tell the audience what we're talking about on Tuesday, because you know, given when this gets produced, right?
16:34
SPEAKER_05: It's it's the big, you know, contact center world uh uh thing.
16:37
SPEAKER_05: But go ahead.
16:38
SPEAKER_05: You know, yeah.
16:39
SPEAKER_01: So we'll be yeah, we'll be speaking at Customer Contact Week Tuesday, and uh we're talking about igniting performance using analytics, which at the end of the day comes to people.
16:48
SPEAKER_01: But here's what I tell people.
16:49
SPEAKER_01: So my I like to use some analogies once in a while.
16:53
SPEAKER_01: Any of you have PowerPoint?
16:54
SPEAKER_01: Did you buy Microsoft PowerPoint?
16:56
SPEAKER_01: When you buy this, when you buy this tool, it's not making your PowerPoints for you.
17:02
SPEAKER_01: You still have to do the work.
17:04
SPEAKER_01: And unfortunately, these AI companies are out there saying, I'm gonna solve all the world's problems, it's a plug-and-play solution, which is a lie, right?
17:12
SPEAKER_02: Right?
17:13
SPEAKER_01: And that's where I come down to that people, right?
17:16
SPEAKER_01: How many interactions in the IT guy who's gonna code this machine?
17:20
SPEAKER_01: How many did he listen to?
17:22
SPEAKER_01: None, right?
17:23
SPEAKER_01: Deliver customer experience that really matters.
17:26
SPEAKER_01: Matt Rocco and I published a book, Delivering Customer Experience That Matters.
17:30
SPEAKER_01: You got to look at those things because it's not what we think in a boardroom.
17:33
SPEAKER_01: It's not what we think in IT, it's not what marketing always thinks.
17:37
SPEAKER_01: You have the data.
17:38
SPEAKER_01: You're sitting on the largest data set on the planet.
17:42
SPEAKER_01: And if you use it correctly, your customers will tell you what they like about you, what they hate about you.
17:47
SPEAKER_01: And I'm with you, Chris, on the surveys, NPS.
17:50
SPEAKER_01: We created voice of customer analysis, which is a little different because guess what?
17:55
SPEAKER_01: I'm looking, we're we are looking at all interactions, not just the people who said, I hate you, I love you, or bribe me with your Starbucks gift card so I fill out your survey.
18:05
SPEAKER_05: Yeah, exactly.
18:06
SPEAKER_05: The the those are interesting points of data, but they're not anything to pivot your entire organization on as a metric or measure of success.
18:17
SPEAKER_05: They can't be, right?
18:18
SPEAKER_05: You're because you know, survey data is oh yeah, it's a bunch of false positives.
18:23
SPEAKER_05: Yep, I'm sorry.
18:24
SPEAKER_05: Absolutely.
18:24
SPEAKER_05: I think it's you know, the people that take them are the people that are pissed, and the people that won't take them are the people that are happy.
18:31
SPEAKER_05: And wait, the people that are pissed, how many things, how many people are they going to recommend?
18:35
SPEAKER_05: Oh, yeah, zero.
18:36
SPEAKER_05: So, you know, I mean, it's it's ridiculous that we have this kind of stuff.
18:41
SPEAKER_05: I think that's, I mean, and I'm I'm sure Seth's want, you know, wants to jump in around, like, you know, on the data science side and you know, how to structure data and all that kind of stuff because it's super important because you can't just, you know, I mean, yeah, a mount of data is a mountain of data.
18:54
SPEAKER_05: However, you can also, you know, get misindication out of that data too, if you're not looking at it or processing it or structuring it for the other thing.
19:04
SPEAKER_02: So to that point, I I want to uh go back on the insights and the understanding.
19:11
SPEAKER_02: And when you look at this data, and we're we had a conversation about this the other day, you know, you have mechanisms and methods to extract more insights than your average bear, right?
19:22
SPEAKER_02: Than your average call center uh analyst.
19:24
SPEAKER_02: Do you want to talk a little bit about that?
19:26
SPEAKER_02: What does it take to kind of set that up and to understand that and really glean those insights?
19:31
SPEAKER_02: And what types of insights are you are you?
19:33
SPEAKER_01: Yeah, so so if you think about you know transcribing, you know, trans everybody talks about speech to text and they call it AI, which really isn't.
19:41
SPEAKER_01: I mean, if you look back 10, 20 years ago, it's big data, it's the same thing, new new, new little shiny, poor little toy.
19:47
SPEAKER_01: Transcribing transcribing the calls is step one.
19:52
SPEAKER_01: I mean, there's tons of ASR engines out there.
19:55
SPEAKER_01: The real work starts that moment that call is transcribed, because now you need to mine the text to generate to what you're saying, Seth, meaningful data.
20:07
SPEAKER_01: And how do you do it?
20:08
SPEAKER_01: Right?
20:09
SPEAKER_01: So, how you do it is with human intelligence post-COVID.
20:13
SPEAKER_01: You know, most contact centers were 10 years behind tech.
20:16
SPEAKER_01: You know, they were so far behind.
20:18
SPEAKER_01: And I think now they're forced because so many remote workers, that's why you're seeing such a big spurge in in this uh marketplace, because everybody's working remotely, you have no idea what's going on.
20:29
SPEAKER_01: So, but now what's happening, what I'm seeing in the market is they're doing it in an ad hoc way, right?
20:35
SPEAKER_01: So they're collecting these AI tools and little trophies, like I've I've implemented speech, you know, I've done this, I've done that, but there's no way to communicate.
20:45
SPEAKER_01: Yeah.
20:46
SPEAKER_01: And there's and and if you think about it, there's no way that these machines all talk to each other.
20:52
SPEAKER_01: And that's why I go back to like what we've developed is a tool that brings all these tech stacks together.
20:58
SPEAKER_01: So I want to know in one spot for the agent, how am I doing on quality?
21:02
SPEAKER_01: How am I doing answering my phone calls?
21:04
SPEAKER_01: How am I doing on my convert?
21:06
SPEAKER_01: And it's got to be one spot.
21:07
SPEAKER_01: Like if you think about it, um, and I like to use a lot of analogies, right?
21:12
SPEAKER_01: So I forget the exact year, but I think it was uh in the 90s, NASA and Lockheed Martin.
21:19
SPEAKER_01: You know, there was a problem in their imperial metric difference in two database tables, right?
21:26
SPEAKER_01: That's what happened to the Mars climate orbiter back then.
21:29
SPEAKER_01: If you look back at the story, that's what happened.
21:31
SPEAKER_01: So you had two people doing data, two different databases not talking to each other.
21:36
SPEAKER_01: That's the problem.
21:37
SPEAKER_01: And it's no different in the call center.
21:38
SPEAKER_01: Well, I'm not going to NASA.
21:40
SPEAKER_01: I can tell you this.
21:41
SPEAKER_01: If you have five different people pulling in reports and looking at it in different ways, you have much more opportunity for failure.
21:48
SPEAKER_01: And if your data's going out not correct, no one's going to listen to you.
21:52
SPEAKER_01: Right.
21:53
SPEAKER_01: And then on top of that, we're expecting a sales leader, right, whose job is to coach.
22:00
SPEAKER_01: Agents become a data scientist?
22:02
SPEAKER_01: Of course not.
22:03
SPEAKER_01: What e tech has done is we've created coaches who coach 85% of the time.
22:09
SPEAKER_01: And we have a team of scientists and analysts that actually provides them who to coach, what to coach, and how to coach.
22:16
SPEAKER_01: And if you think these people can just pop a button and a dashboard, which these guys all offer, is going to solve your problems, it doesn't work that way.
22:26
SPEAKER_02: So let's talk a little bit about the details behind that in terms of being able to set those things up because your your analytics need to understand something about the language the customers are using and something about the language that the agent is using, something about the products and the services and the solutions and the problems, right?
22:45
SPEAKER_02: Is there calling for problems?
22:46
SPEAKER_02: Why are they calling call center?
22:47
SPEAKER_02: Because something is broken, right?
22:49
SPEAKER_02: They can't get an answer, they can't find something.
22:51
SPEAKER_02: So what goes into, and you know, I'm kind of leading the witness here, right?
22:55
SPEAKER_02: What goes into setting those things up?
22:57
SPEAKER_02: I mean, you do need control of vocabularies, you do need an architecture, a reference architecture.
23:02
SPEAKER_02: You want to talk about that.
23:03
SPEAKER_02: And when you go into organizations, how well prepared are they in some of those areas?
23:08
SPEAKER_01: So it's a great question because I think there's so much confusion on the marketplace.
23:13
SPEAKER_01: I think they think they're confused because the software companies are telling them it's plug and play and it's not.
23:18
SPEAKER_01: And they're saying I'm going to set it all up for you in 90 days, which they do.
23:22
SPEAKER_01: But again, you got IT people doing it.
23:25
SPEAKER_01: When I talk to customers, I talk about our engineered knowledge framework from the get-go because I've already done it right.
23:32
SPEAKER_02: That's where it's going next, is your knowledge knowledge based.
23:35
SPEAKER_01: So you got this knowledge framework, right?
23:38
SPEAKER_01: That actually works.
23:40
SPEAKER_01: And you've you've already deployed it and you created categories.
23:43
SPEAKER_01: And the another, I go back to analogies.
23:46
SPEAKER_01: In case you didn't know this, there's over 420 different ways and sentiments for the word good.
23:52
SPEAKER_01: In a machine, good is not always good.
23:54
SPEAKER_01: Because if I'm Charlie Brown, which I love, he and it's good grief is not good.
23:59
SPEAKER_01: So your engineer has to be able to answer your question, create a knowledge base and look at natural language processing and have to look at sentiment, and you have to look at what's going on before and after the word that was said.
24:14
SPEAKER_01: Another one, like Chris, you talked about in the beginning, accent, right?
24:18
SPEAKER_01: Accents and listen, as you can tell, I'm not from Texas with this huge Texas accent, right?
24:24
SPEAKER_01: But if I say I'm getting my oil changed, right?
24:27
SPEAKER_01: Um, if I'm getting my oil changed, that transcript probably will say ALL.
24:33
SPEAKER_01: But a data scientist knows that if they see I'm going to get my ALL changed, they know that in this context, it's oil.
24:43
SPEAKER_01: Right, right, right.
24:44
SPEAKER_04: I think I think they can be changed.
24:46
SPEAKER_02: I just want to uh interrupt for a moment.
24:48
SPEAKER_02: I wanted to let people know that um uh we're about halfway through our time, and I wanted to remind listeners that we're talking with uh with call center expert and customer experience expert Jim Ayu.
25:00
SPEAKER_02: So let's carry on.
25:01
SPEAKER_01: Thank you, sir.
25:02
SPEAKER_01: We get so passionate, we get so passionate about conversations, it's so much fun.
25:07
SPEAKER_02: We'll be right back.
25:09
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25:14
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25:24
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25:34
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25:41
SPEAKER_02: CX Decoded is a semi-monthly podcast designed to deliver insight from leaders driving the evolution of customer experience with an emphasis on digital touch points, customer journeys, respectful data management, and voice of the customer.
26:00
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27:50
SPEAKER_05: Well, so Jim, there was there was um, I think the one thing too that that you know we need to double click on as well is there's there's definitely um, you know, human intelligence is first, let's figure out what the outcome is we want to drive.
28:08
SPEAKER_05: Second, let's set an hypothesis, ask questions.
28:10
SPEAKER_05: Third, let's go find if the data is there.
28:13
SPEAKER_05: And then fourth, let's actually make insightful decisions on top of what we initially are processing.
28:19
SPEAKER_05: The one thing, too, that I want to make sure that people understand is that you know, I 100% agree with you.
28:24
SPEAKER_05: You know, it's I've seen this before, where an ASR engine goes in place.
28:30
SPEAKER_05: Hell, I, you know, I built it on Amazon, an NLP engine goes in place, and then the business sits there and goes like this.
28:37
SPEAKER_05: Yep.
28:37
SPEAKER_05: Like they're waiting for magic to happen.
28:40
SPEAKER_05: And guess what comes out?
28:42
SPEAKER_05: Oh, it comes back with maybe 70% accuracy, and it comes back with you know flawed issues because they're thinking that it's it's the magic pill, and yet it comes back and it's basically just like you said, it's just the beginning of the beginning, and they don't understand too that a model has to is semantic, like we talked about in the beginning, and it has to be driven with the right data to understand how to drive hierarchy of that model.
29:12
SPEAKER_05: And you have to train and tune and care and feed and label and you know, all that good stuff that takes a lot of work.
29:19
SPEAKER_05: Now, there's ways of doing that faster, but I also I want to make sure that people understand that that an NLP engine and an ASR engine are there to enable people to get more access to insightful data quicker from a driven outcome that's designed.
29:36
SPEAKER_05: And a lot of people will think, well, I'm just gonna throw it at the thing and then just that's that's gonna be it.
29:41
SPEAKER_05: An ASR engine, oh yeah, it comes back with you know low, you know, F1 scores and everything else.
29:46
SPEAKER_05: Like, what the hell?
29:46
SPEAKER_05: What am I doing wrong?
29:48
SPEAKER_05: Nothing.
29:49
SPEAKER_05: It's that what you're doing is you just need to keep pumping more data at it, more data at it, and do the supervised learning to make sure that it's training and tuning for what you need, because there's no way in hell.
29:59
SPEAKER_05: How many, how many you said you did three million calls a month, right?
30:02
SPEAKER_05: Correct, going through.
30:04
SPEAKER_05: And I'll bet your supervisors or coaches don't spend the time trying to sift through and listen to however many percentage of those calls to do the coaching, you know, at all, right?
30:16
SPEAKER_05: Because that's a absolutely a futile effort in an ability to actually then make them more efficient or actually help the agents, right?
30:23
SPEAKER_05: Which I want to get to the agents and incentives in a minute.
30:26
SPEAKER_05: But I think one thing that people need to understand is this technology is there to help create and drive the cohorts and you know, some of these, you know, whether you call a heat map cohort, whether you call it just the anomalies, to then go back in and check.
30:42
SPEAKER_05: Because I love the fact that I can send a machine learning model against all my recordings and come back with a result set that I can look at.
30:50
SPEAKER_05: Okay, that's right, that's not right, send it back and then come back and just have it be processing in a cycle.
30:57
SPEAKER_05: Because I'm not, I don't want my supervisors there or my coaches sitting there listening to calls.
31:01
SPEAKER_05: That's absolutely ineffective.
31:03
SPEAKER_05: And yet we do this all the damn time.
31:06
SPEAKER_02: Yeah, yeah.
31:06
SPEAKER_02: So we prioritize, right?
31:08
SPEAKER_01: Yeah, so we agree with that concept, by the way.
31:11
SPEAKER_01: That's why we created our own data scientists, data analysts, and and the engineers, right?
31:15
SPEAKER_01: So, and by the way, not only the the three million plus calls that e tech is scraping and going through, it's not just for our customers.
31:23
SPEAKER_01: E tech actually sells our analytics as a, you know, we do it for other companies, right?
31:28
SPEAKER_01: But to answer your question about the machine, we we know how the machine works.
31:31
SPEAKER_01: That's why we're in the people business, by the way, because you always need people.
31:34
SPEAKER_01: Technology is thought, it doesn't matter who you buy, you still need it.
31:37
SPEAKER_01: So our benchmarks are built on Six Sigma world leaders to ensure you identify those gaps, pump that data in, and get to about 85% accuracy.
31:50
SPEAKER_01: Now, there are companies out there that will tell you they're at 92, 93% accuracy.
31:55
SPEAKER_01: And yes, have I seen it?
31:56
SPEAKER_01: Yes.
31:57
SPEAKER_01: But to your point, the only way that's going to happen if you have maintenance going on.
32:03
SPEAKER_01: So we have full-time, right, data analysts and engineers.
32:08
SPEAKER_01: That engineer's job every day, instead of him listening uh or or instead of our coaches listening to calls all day, we have individuals who actually make sure the engine's hitting correctly.
32:19
SPEAKER_01: Yeah.
32:20
SPEAKER_01: And they have to do subsets, listen to a call, and make sure they're hitting.
32:23
SPEAKER_01: So, like as an example, if you did the word ATT, ATT is a known brand, but you know that no text, no, no SAR engine's picking up ATT, it's going to come across eight, 80, 18, and those people go in and fine-tune those, and that's their only job.
32:37
SPEAKER_01: But they spend eight hours a day doing that to get those 85%.
32:41
SPEAKER_01: The other thing I'll talk about when you talk about agents, here's what I also found out.
32:46
SPEAKER_01: If you go to an agent and say, hey, listen, and this is the coach, right?
32:50
SPEAKER_01: Because I agree with you.
32:51
SPEAKER_01: Our coaches only listen to a couple calls, but we give them the data.
32:54
SPEAKER_01: Hey, Chris, listen to 150 phone calls.
32:58
SPEAKER_01: You're only effective on probing 62% of the time.
33:04
SPEAKER_01: It's a different conversation.
33:05
SPEAKER_01: Because now I'm not saying I listen to a call.
33:10
SPEAKER_01: We listen to hundreds.
33:11
SPEAKER_01: And it's about the accuracy, even if I'm off by 10%, you're still not where you need to be.
33:19
SPEAKER_01: Right?
33:19
SPEAKER_01: And that's the difference of the conversations.
33:22
SPEAKER_01: And if you deploy that properly, agents actually love that.
33:27
SPEAKER_01: Because now you're being transparent, you're being a servant heart, and you're actually trying to get them when you talk about incentives in the future, more money.
33:36
SPEAKER_01: You want to make more money?
33:38
SPEAKER_01: Let me show you your other person who does these behaviors because I don't think quality is a score.
33:44
SPEAKER_01: Matt and I wrote a book, our first quality book that we came out.
33:48
SPEAKER_01: Quality's not a score, it's a behavior, right?
33:50
SPEAKER_01: So if you focus on behaviors and we identify three key performance behaviors, and we have a key performance behavior index score, which encompasses lots of them.
34:02
SPEAKER_01: But we tell the agent three things, focus on these three, get those moving the needle, then work on some other ones.
34:09
SPEAKER_01: Because you know, you can't tell an agent the 50 things they did wrong either.
34:13
SPEAKER_05: Right.
34:14
SPEAKER_05: No, I mean that's that's the key, that's that's where I wanted to go next is you know, accountability or sorry, transparency drives accountability.
34:22
SPEAKER_05: Yep.
34:23
SPEAKER_05: However, the incentives that we put in place to try to you know create, you know, a great experience, humans gain incentives.
34:32
SPEAKER_05: That's that's let's throw that fact out there.
34:35
SPEAKER_05: Now, the if you can get the transparency, you can drive the right accountability.
34:40
SPEAKER_05: If you can drive the right accountability, then you might be able to get to the right incentives that incent them to actually really think about what you're saying, remove the shackles, because the the you know, like quantitative metrics and qualitative metrics generally don't fit together in a you know one plus one equals two type of scenario.
35:00
SPEAKER_05: So, you know, and to your point, like, hey, I want to have all these KPIs to drive my quality metrics, but that's also very semantic towards the organization.
35:09
SPEAKER_05: But I think that's the one thing that I want to, you know, I want you to, I'd love for you to double-click on is the right ways to help organizations understand how they should build the right incentives so that they don't have to do all the babysitting or script adherence or any of that other bullshit that that just basically, you know, and then utilizing, you know, some of these artificial intelligence things to actually drive that transparency to help them, as opposed to giving them, you know, uh an A B or A through F score every call.
35:42
SPEAKER_05: You know what I mean?
35:44
SPEAKER_05: Let's talk about how you guys, how you guys, you know, at eTech or just you in general, think about that from the perspective of helping organizations get from strategic goals in through to the right incentives to make sure that they're driving the right behaviors.
35:57
SPEAKER_01: Yeah, it's a great question.
35:58
SPEAKER_01: So sales ones are pretty easy.
36:00
SPEAKER_01: So I we do a lot of sales, right?
36:02
SPEAKER_01: So first and foremost, I'm listening to all calls, number one.
36:07
SPEAKER_01: I'm doing a hundred percent sales verification on sales using AI, which actually avoids people gaming the system.
36:18
SPEAKER_02: What does that mean?
36:19
SPEAKER_01: 100% so basically every time their agent has sold a product, service, whatever it is, I'm recording the we're recording the call 100% cradle to grave.
36:30
SPEAKER_01: And these we've coded our machine to say that you had to say the price was correct, which we can code for.
36:36
SPEAKER_01: You had to say what was happening to their bill, which we can code for.
36:40
SPEAKER_01: And basically what it does is so they don't game the system.
36:43
SPEAKER_01: Hey, I got a sale, let's validate that it was a it's it's almost like third-party verification, similar, but we're doing it with AI.
36:52
SPEAKER_01: And then what happens is if the sale is rejected, it actually goes back to the agent and their supervisor within seven to ten minutes so that the agent can actually recall that person.
37:03
SPEAKER_01: Yeah, because sometimes it's you know, we get it, sometimes it's a little misunderstanding, so you just so they got the chance to resell it.
37:10
SPEAKER_01: And the in and that's how we that's how we, and by the way, however we get paid, we take a percentage of that and pay to our agents.
37:17
SPEAKER_01: So if a customer comes to us and says, I want everybody in top two CSAP box as an example, Chris.
37:23
SPEAKER_01: I'll say, all right, so we'll dig down a little further and say, so tell me what you're trying to accomplish.
37:28
SPEAKER_01: Yeah, because I'm we are you and I already know only four to 17% at max is gonna fill that survey out.
37:35
SPEAKER_01: So tell me what you're what what's the behaviors you're trying to?
37:38
SPEAKER_01: We want it to be effortless.
37:40
SPEAKER_01: We have an effortless driver.
37:42
SPEAKER_01: I can show you on hundreds of calls, not three, on hundreds, effortless experience.
37:49
SPEAKER_01: And we'll and we'll we'll create a compensation plan about a score right around that because it's gonna be looking at all of the interactions.
37:57
SPEAKER_01: So that's what we do in CX.
37:59
SPEAKER_01: In the save cues, here I'll find an interesting one for you.
38:02
SPEAKER_01: I I did a about four years ago, I did it for a major subscription service.
38:07
SPEAKER_01: And he came to me with a problem and he says, Hey, I'm having a problem with my cancellations.
38:13
SPEAKER_01: I said, Let me look.
38:15
SPEAKER_01: Long story short, I dropped in like 5,000 calls, went in and looked.
38:21
SPEAKER_01: And it's funny you say the part about incentive, and this is where my story comes from.
38:27
SPEAKER_01: I was like, dude, like 85% of your people aren't even trying to save.
38:32
SPEAKER_01: Like literally, they're saying, Oh, yeah, you want to cancel your subscription?
38:36
SPEAKER_01: No problem, I'll take care of that for you.
38:38
SPEAKER_02: Yes, yes, yes, yes.
38:39
SPEAKER_01: And I said, so we were back with this whole, which probably wasn't needed, but it was funny because he is my he's been my customer for six years, referenceable client.
38:47
SPEAKER_01: But it was so funny because I sent him the data and I said, Listen, here's the three things you need to do.
38:52
SPEAKER_01: You need to have an incentive plan to save the customer.
38:54
SPEAKER_01: And here's our recommendations, right?
38:56
SPEAKER_01: You need to teach these people that it's okay to cancel, but the first thing you should do, and he's like, I hate to tell you this, but we pay people to save.
39:03
SPEAKER_01: I said, that's just killing me now.
39:05
SPEAKER_01: It's like, so you so so then what happened?
39:08
SPEAKER_01: The remarkable part of how it fixed it, they were coaching the call.
39:13
SPEAKER_01: Hey, I listened to a call and you didn't save, you need to send.
39:17
SPEAKER_01: So the agent's mind is yep, yep, yep, you caught me.
39:19
SPEAKER_01: I got that call.
39:20
SPEAKER_01: When we deployed this for them, we were able to show them, hey, out of 1,100 calls in a month, you only save 1%.
39:28
SPEAKER_01: And according to this customer, he told me each one percent save is equal to a million dollars a year for him because it's a subscription service.
39:37
SPEAKER_01: So once we when we taught the agents that we were going to be listening to all saves, and we're gonna we're gonna show you, and we we don't say the score, it's effective.
39:47
SPEAKER_01: We call it an effectiveness rate.
39:49
SPEAKER_01: You're only effective in saves 30 percent of the time, and we want you to be at about 60.
39:55
SPEAKER_01: We know you can't do it all the time, that's unrealistic, but we want and you give them guidelines, right?
40:00
SPEAKER_01: That you want to attempt to save because you you know this as well as I know.
40:04
SPEAKER_01: You're not gonna save everybody, but you can attempt to save everyone, right?
40:10
SPEAKER_05: It's it's script compliance and not script adherents, right?
40:12
SPEAKER_05: Is what we're talking about.
40:14
SPEAKER_05: Go ahead.
40:15
SPEAKER_02: Question.
40:15
SPEAKER_02: So um, you know, a lot of the times you you talk about you have to go into an organization, you have to give them that knowledge framework.
40:22
SPEAKER_02: Where are they?
40:23
SPEAKER_02: Where are you?
40:24
SPEAKER_02: What are you seeing these days in terms of uh because we know that you know you have to power virtual assistants with knowledge, you have to power your your agents with knowledge, right?
40:33
SPEAKER_02: You have to power self-service with knowledge, all of that is so important.
40:37
SPEAKER_02: But you know, our experience is that this so siloed and fragmented.
40:40
SPEAKER_02: So it would seem that the call center is the place where that could be pulling pulled together, but it also seems like it's another silo.
40:48
SPEAKER_02: So, what are you seeing in organizations when you go in and you start looking at okay, where are your knowledge bases and how are you supporting this?
40:54
SPEAKER_02: Give me a sense of the picture that you see.
40:57
SPEAKER_01: Yeah, it's crazy.
40:58
SPEAKER_01: Like that's where your expertise would would be very valuable to a lot of these companies.
41:02
SPEAKER_01: The companies I talk to, it's and by the way, e text one of them.
41:07
SPEAKER_01: I'm not, you know, saying that we're terrible, but I mean, if you look at a knowledge base, let's be honest.
41:14
SPEAKER_01: Your customer hired you, gave you a PowerPoint presentation, right?
41:18
SPEAKER_01: If you're lucky, if you're extremely lucky, you got a facilitator guide, right?
41:24
SPEAKER_01: And if you're if they're really a well-versed machine, they gave you a URL that hasn't been updated in five years with your FAQs.
41:33
SPEAKER_01: And it and the reason it is siloed, in my opinion, is different people own those to your point.
41:39
SPEAKER_01: Right.
41:39
SPEAKER_01: But I think the holy grail is if you can combine, like I did with my quality and call center metrics into one platform, right?
41:48
SPEAKER_01: So the agent doesn't have to go 50 different places.
41:50
SPEAKER_01: And I haven't gotten to the knowledge base part of it yet, but that's like my on my roadmap.
41:55
SPEAKER_01: How do you tie all of that stuff together?
41:57
SPEAKER_01: But to answer your question, it's amazing the disparate systems that all these companies are using.
42:04
SPEAKER_01: And I think it's to answer your question about the call center, it should be the call center.
42:09
SPEAKER_01: The call center should be able to get you the most information because by the way, that's how we build out our stuff, listening to all those calls.
42:17
SPEAKER_01: If you run those, you have the knowledge base, but the guy in but the but remember the guy in ops, not his job.
42:25
SPEAKER_01: What's his job?
42:26
SPEAKER_01: Coach the agent, right?
42:28
SPEAKER_01: So that's why it's siloed.
42:31
SPEAKER_01: Um, and that's where I think there's an opportunity in our space, because and you and I talked about this in the past.
42:38
SPEAKER_01: When you look at all of these disparate systems, if you had one person, and I know we we none of us have a closet and and of people just sitting there not doing any work, right?
42:49
SPEAKER_01: So it's hard anyway.
42:50
SPEAKER_01: We should we shouldn't, but I think it's hard to get the people because because to do a a a real true knowledge pull, which you know you can do with APIs, you literally have to have someone full time to do it.
43:04
SPEAKER_01: It might take 30 days, 60 days, it's not like it's the rest of their lives, but you know, you really need to have a dedicated person to come in and look at it from a different perspective.
43:15
SPEAKER_01: Because if you're touched to the data like me, I'm close to my data and I know my data very well, but I don't know other people's data.
43:23
SPEAKER_01: I never met a data I didn't like.
43:25
SPEAKER_01: Correct.
43:26
SPEAKER_01: I've never met a data I didn't like, but you know what?
43:28
SPEAKER_01: The problem the and I love that one, the problem we have is it's like marketing sometimes doesn't like to talk to sales.
43:37
SPEAKER_01: And if marketing puts something out, you know it's all legal from a knowledge base.
43:41
SPEAKER_01: So you sound script it as we talked about, which is terrible for the agent.
43:45
SPEAKER_01: And then if you talk to the if you talk to the CEO or the CFO or those guys are all about listen, you can you can shave four minutes off my call just by building this knowledge base.
43:55
SPEAKER_01: How much is the knowledge base gonna cost me?
43:56
SPEAKER_01: When's it gonna, you know, that you have to get all of them in a room.
44:00
SPEAKER_01: And then you have to start with with which my boss taught me a long time ago.
44:04
SPEAKER_01: Go into a room and the first rule of thumb is none of you have titles.
44:08
SPEAKER_01: We're all the same.
44:09
SPEAKER_01: Right?
44:10
SPEAKER_01: Because now you can't fight with each other.
44:12
SPEAKER_01: You basically say it's it's how do you lead that leadership to think differently and put a plan of action and then assign the action to an individual, not a team.
44:22
SPEAKER_01: An individual that's going to say, your job is to take us from the 18th century to the 20th century with knowledge base.
44:31
SPEAKER_01: Right.
44:32
SPEAKER_02: And it's a good point when you say, you know, don't assign it to a team.
44:35
SPEAKER_02: When everybody's responsible, nobody's responsible, right?
44:38
SPEAKER_02: Yep.
44:38
SPEAKER_02: We also do a uh have some approaches for actually creating those knowledge bases and um being able to power and drive the capabilities of a virtual assistant or a bot with that mining of the knowledge through your tools.
44:54
SPEAKER_02: I want to talk a little bit about that.
44:55
SPEAKER_02: When does that work really well and when does it not work well?
44:58
SPEAKER_01: Yeah, great question.
44:59
SPEAKER_01: So it works really well when you use those interactions.
45:03
SPEAKER_01: So as you know, I'm I have developers who are mining the data and training the bots.
45:11
SPEAKER_01: So if you look in some of the examples I have is like if you ever went to United, their bots are terrible.
45:16
SPEAKER_01: I think her name is Alex, worst bot I've ever known, a man.
45:19
SPEAKER_01: You ask it a question like a guy who flies all the time, knows nothing, right?
45:24
SPEAKER_01: You know, and when you look, and so to answer your question, if you mine the data, forget what marketing tells me that they think the problems are, or the IT guy who says, I think this is the problem based on my switch.
45:36
SPEAKER_01: You have the biggest data set on the planet, as we said.
45:39
SPEAKER_01: Actual interaction, people solving problems, solving problems, the best of the best reps, which could pull out all of that information to create an unbelievable assistant.
45:51
SPEAKER_01: So I've done I've deployed one on my website recently, and it's funny you say that because it's not my technology.
46:00
SPEAKER_01: I I put a bot out for our recruiters recently, and the spreadsheet came through, it must have been 26 pages long of all the messages.
46:11
SPEAKER_01: And you know where I got all the messages from?
46:14
SPEAKER_01: The phone calls that my recruiters were doing every day.
46:17
SPEAKER_01: Okay, yeah, and it literally took us.
46:20
SPEAKER_01: I when I took all the messages, we took the top 10 of all the ones that were coming in, sent it out to the executive team who owned the departments, right?
46:29
SPEAKER_01: So if somebody was asking, what are your benefits?
46:32
SPEAKER_01: Well, I don't know benefits, that's not me.
46:34
SPEAKER_01: So we got all the questions that were asked with all of our responses that we were giving from the recruiting side, gave it to HR.
46:42
SPEAKER_01: They went through it in literally about a couple hours, made some nice fine tunes for us, and now we program the bot.
46:49
SPEAKER_01: That is so much easier than saying, Hey, listen, let's sit down and write down all these responses.
46:53
SPEAKER_01: What are the possible responses?
46:56
SPEAKER_01: And that's where AI speaks the text helps build.
47:00
SPEAKER_05: Right.
47:00
SPEAKER_05: Arguably, bots are way better than they were in the past, agreed neurons and things like that, because you're right.
47:09
SPEAKER_05: I mean, you know, nobody ever wanted to use a bot before and would pivot on voice and all that kind of stuff as a backup backstop to the channel.
47:18
SPEAKER_05: But you know, that was uh it was it's been really interesting because also the text-to-speech has also gotten way better.
47:25
SPEAKER_05: So and it's not that I don't, you know, I I mean, even if it's robotic, that's fine if it's solving my problems, whatever.
47:34
SPEAKER_05: Correct.
47:35
SPEAKER_05: But, you know, as we are, we're driven by, you know, our bias is driven by the best consumer experiences out there, and we've got all these damn pockypucks sitting on our desks and in our home with um, you know, voice command-based assistance.
47:50
SPEAKER_05: They're bad, they're chatbots, regardless.
47:52
SPEAKER_05: If people don't they just have local ASR in DSPs doing the processing and stuff so they can actually pick it up.
47:59
SPEAKER_05: But you know, it's all that's also thank goodness, level the playing field for a lot of these folks to go back and say, Well, yeah, I deal with, you know, I won't say her name or his name or whatever else because all the devices will go.
48:13
SPEAKER_05: But now it's you know more acceptable.
48:15
SPEAKER_05: But to your point, and I I'm glad Seth picked up on that because I was gonna ask that too, in terms of your guys' stance on digital assistance, is you know, it doesn't matter, they're only as good as the data is, and I think that's what people have to figure out.
48:29
SPEAKER_05: And I'm with you, that's what we do is take all these conversations from the call center.
48:33
SPEAKER_05: Like, I can't, I can't, you know, understand why somebody would give an intent builder to some business analyst that just sits there and says, you know, uh, hey, here's what I think they should say.
48:45
SPEAKER_05: Well, good lord, I've got millions of conversations of both positive, negative, solving problems, not solving problems to actually, you know, tune it.
48:54
SPEAKER_01: Yeah, one thing I one thing I'll bring up for you in that, which I think is a great case study.
48:57
SPEAKER_01: And next week when I speak in Vegas, this customer speaking on stage with me.
49:01
SPEAKER_01: Cool.
49:02
SPEAKER_01: So we were fortunate enough to win the call center work, right?
49:05
SPEAKER_01: Because he wasn't really happy, right?
49:07
SPEAKER_01: He came to me and I talked, you know, he's like, What, you know, as usual, what differentiates you from everybody else?
49:12
SPEAKER_01: Analytics, data, and how I use it.
49:14
SPEAKER_01: So that's pretty much what sold him, right?
49:17
SPEAKER_01: He's like, I'm gonna give you a shot.
49:19
SPEAKER_01: So they hired us for to answer his calls and his chats.
49:23
SPEAKER_01: And he sent over his training information.
49:26
SPEAKER_01: And I said, Before you do that, can I get 10,000 phone calls from you?
49:31
SPEAKER_01: You already make so he did.
49:33
SPEAKER_01: He sent us 10,000 calls.
49:35
SPEAKER_01: We my team analyzed it, built it all together, built all the playbooks, right?
49:40
SPEAKER_01: And they still sent their training information.
49:42
SPEAKER_01: When I sent the readout to them, they said, throw away our training information.
49:46
SPEAKER_01: Let's use what you guys got because yours is actually much, you know, because because we knew exactly what was happening.
49:51
SPEAKER_01: Wow, and uh, we just did it, we just did their first 30-day readout yesterday.
49:56
SPEAKER_01: Um, that's why I'm so excited to be to be talking with these guys on stage because we just did their first readout yesterday, and the insights were showing them what people are saying.
50:05
SPEAKER_01: They thought and they tell you this man, I thought that was a problem, but now you've quantified it.
50:12
SPEAKER_01: Right.
50:13
SPEAKER_01: So now things can get done.
50:14
SPEAKER_01: So, so that's that's one of the things as you talk about gathering that data, it's great.
50:19
SPEAKER_02: Yeah, I have a quick uh logistics question.
50:21
SPEAKER_02: How do you deal with PII in that situation?
50:24
SPEAKER_01: So, yeah, absolutely.
50:26
SPEAKER_01: So most decent ASR engines can redact in transit and at transit and then rest, right?
50:34
SPEAKER_01: So that's like the first thing I will tell.
50:36
SPEAKER_01: And I'll be talking about PII information.
50:38
SPEAKER_01: It strips out your personal identical information.
50:41
SPEAKER_01: But by the way, there are ASR engines that don't do that.
50:44
SPEAKER_01: So make sure it does have that capability.
50:46
SPEAKER_02: And and when an organization is doing this, um, you know, is there an issue around security with on-prem versus cloud?
50:54
SPEAKER_02: You have to meet certain security requirements if you're going to run your engine in the cloud.
50:58
SPEAKER_02: I imagine that's how it's going to happen, right?
51:00
SPEAKER_02: So tell me how that works.
51:02
SPEAKER_01: Yeah, so I we have a chief security officer that does all that.
51:05
SPEAKER_01: Jim's opinion, the cloud and the server in my desk, it's the same exact thing, it's just a location.
51:11
SPEAKER_01: So you have to have the same security protocols.
51:14
SPEAKER_02: I totally get that.
51:14
SPEAKER_02: I mean, I get that, but I know a lot of organizations.
51:16
SPEAKER_01: No, no, you're absolutely right.
51:18
SPEAKER_01: They always talk about what about security?
51:20
SPEAKER_01: It's in the cloud.
51:21
SPEAKER_02: Yeah, you mean it is a matter of you simply have security protocols and then you do it.
51:26
SPEAKER_05: It's yeah.
51:28
SPEAKER_02: I'm sorry, Chris.
51:28
SPEAKER_02: Go ahead.
51:29
SPEAKER_05: Well, I was just gonna say oftentimes now, Seth, there's shared responsibility models that are in place that actually give, you know, whether it's it's high availability fault tolerance or security, then they are generally better in the cloud than they are on-prem anyway.
51:44
SPEAKER_05: Right.
51:45
SPEAKER_05: So it's yeah, yeah.
51:46
SPEAKER_02: Yeah, I had I had another question.
51:48
SPEAKER_02: You mentioned before that you know, when people have certain problems or challenges, you know, you're saying, well, it's not their job to necessarily fix it.
51:56
SPEAKER_02: So when you think about the call center, call centers are a great place uh to find those problems because again, why are people calling?
52:02
SPEAKER_02: Because something is broken somewhere.
52:04
SPEAKER_02: And but many times the call center doesn't have responsibility or the authority or the mandate or whatever to go upstream and fix those things.
52:13
SPEAKER_02: What do you do with organizations who have that when you say, you know what, here's I mean, it's almost a counterproductive in a way or cannibalizing your business if you go and fix things to reduce the call center volume, right?
52:24
SPEAKER_02: If you you do that call deflection, how do you kind of balance that and deal with that and and and message around that?
52:30
SPEAKER_01: Yeah, so as a trusted advisor, if our call volume drops and I add value to the customer to win for e tech, right?
52:36
SPEAKER_01: So I'll give you a live example.
52:38
SPEAKER_01: So we had a bunch of calls coming in and chats asking certain things, which by the way should have been self-serve.
52:45
SPEAKER_01: Okay.
52:46
SPEAKER_01: So to give you the example, now my call center agents aren't seeing what we see because they're just like, man, I'm getting all these non-qualified calls, right?
52:54
SPEAKER_01: Because it's a sales queue.
52:55
SPEAKER_01: And that's all they think.
52:57
SPEAKER_01: When you get an analyst or you send an analyst in, tell me what you're hearing.
53:01
SPEAKER_01: And we're finding somebody wants copies of receipts.
53:04
SPEAKER_01: That should never be coming through a sales queue.
53:06
SPEAKER_01: Now, if you go to your vendor manager who's in ops at that company and you tell them, hey, we're getting a lot of calls, they're gonna say you're giving me an excuse why you're not converting.
53:16
SPEAKER_01: When you take a business analyst like myself and my team, I go and say, Hey, Seth, out of the 17,452 calls, right, we took this month, 22% were getting a copy of the receipt.
53:30
SPEAKER_01: And by the way, Mr.
53:31
SPEAKER_01: IT guy, you told me it's on your website.
53:33
SPEAKER_01: I went out there.
53:34
SPEAKER_01: And it reminds me of the the Tootsie Pop commercial where it's how many licks does it take to get the center of a Tootsie Pop?
53:40
SPEAKER_01: It took me nine clicks to get my receipt.
53:42
SPEAKER_01: That is not effortless.
53:44
SPEAKER_01: And when you give them data like that, yeah, that's how it works.
53:48
SPEAKER_01: But I I don't think that when I say it's not their job, they're so busy doing other things.
53:53
SPEAKER_02: I think their job is to tell someone in the organization who could make an impact and take it and take it for the next thing in the right process to take that and that closed loop learning and saying, okay, how are we going to go upstream and fix some of those things?
54:09
SPEAKER_01: We've had some, I'll give you another one real quick.
54:11
SPEAKER_01: A telco that we do business for.
54:14
SPEAKER_01: Uh, we had like 185 calls in queue, and everybody's setting up all these IT lines, right?
54:19
SPEAKER_01: They're all setting up IT lines.
54:20
SPEAKER_01: Oh my god, there's something going on, something wrong with the switch.
54:23
SPEAKER_01: I went into the to the switch and says, Well, let's look, let's.
54:26
SPEAKER_01: And what I you went in and used the browse and search capability, it told me that the one of their websites was down because the people were saying, Hey, I'm calling you because I was trying to do this on the website, is down.
54:36
SPEAKER_01: We were able to give it before the IT guys even started tracing all their problems.
54:40
SPEAKER_01: Within five minutes, we said, Hey, here's the page that's broke, here's where you go, go fix it.
54:45
SPEAKER_02: Here's the indicator, yeah.
54:46
SPEAKER_02: But you're taking that signal, interpreting it, feeding it back, and lines here are the front lines.
54:53
SPEAKER_02: Hey, we're almost at the top of the hour.
54:55
SPEAKER_02: And you know, Jim, this has been great.
54:57
SPEAKER_02: I always enjoy our conversations and always enjoy conversations with Chris.
55:01
SPEAKER_02: Anything that you want to kind of leave people with to um kind of sum up, or what are your words of wisdom?
55:07
SPEAKER_02: What what kind of things do you want to leave people with in this world that's changing so quickly and with virtual assistants and that kind of future?
55:15
SPEAKER_02: You know, we have this future.
55:16
SPEAKER_02: I wrote about it in the book where these things are going to be everywhere, these virtual assistants and digital workers and so on.
55:22
SPEAKER_02: What what's your advice or what's your uh what's your uh what's in your crystal ball?
55:27
SPEAKER_01: Yeah, so my crystal ball is simple.
55:30
SPEAKER_01: Um, I I would tell people to be a life learner.
55:35
SPEAKER_01: I did not, I came from ops, as you said in my bio.
55:38
SPEAKER_01: I started as an agent, okay, and a supervisor.
55:42
SPEAKER_01: I read an article a day.
55:44
SPEAKER_01: That's my motto.
55:45
SPEAKER_01: Okay.
55:45
SPEAKER_01: Um, I am a self-learner.
55:47
SPEAKER_01: I would tell people to continue to self-learn.
55:49
SPEAKER_01: I didn't know AI when I started into it.
55:51
SPEAKER_01: Now I'm considered an expert.
55:53
SPEAKER_01: By the way, in the beginning, Chris mentioned uh MIT.
55:56
SPEAKER_01: So my director over my analytics graduated from MIT with data scientists, and he did that on his own, by the way.
56:05
SPEAKER_01: Right.
56:05
SPEAKER_01: So so yeah, so that would be the first thing I would say is always be a self-learner.
56:10
SPEAKER_01: And self-learner doesn't mean you have to go to 50 different schools.
56:14
SPEAKER_01: Read an article a day to start, you'll become a master in a year.
56:17
SPEAKER_01: Number two, take what you do seriously, don't take yourself seriously.
56:23
SPEAKER_01: Seth, you and I've been hanging out for, and I love my conversation with you.
56:26
SPEAKER_01: I am passionate about what I do, but I don't take myself so corporate because I think in this corporate world we've lost the personality.
56:35
SPEAKER_00: Right?
56:36
SPEAKER_01: You know, and then I would say, Yep, thank you.
56:39
SPEAKER_01: And I would say deliver customer experience that matters, right?
56:44
SPEAKER_01: Stop looking to solve all problems.
56:47
SPEAKER_01: People make mistakes, and you learn from those mistakes.
56:51
SPEAKER_01: Like my boss will tell you, I make hundreds of mistakes a week, right?
56:54
SPEAKER_01: That's great, but I'm still here.
56:55
SPEAKER_01: I've been with them for 30 years.
56:57
SPEAKER_02: I say there's no losing, there's winning or learning.
57:00
SPEAKER_01: Correct.
57:00
SPEAKER_01: Winning or learning.
57:01
SPEAKER_01: I love that.
57:02
SPEAKER_01: You're absolutely right.
57:03
SPEAKER_01: Don't be afraid to fall down and get up, right?
57:08
SPEAKER_02: Jim, thank you so much.
57:10
SPEAKER_02: Uh, this has been fantastic as usual.
57:12
SPEAKER_02: Really glad to have you.
57:14
SPEAKER_02: And uh again, uh Jim Ayub, uh, he's uh Chief Customer Officer at eTech Services.
57:21
SPEAKER_02: And um again, congratulations on the CX Hall of Fame, and thank you so much for your participation.
57:27
SPEAKER_02: And thank you, Chris, and of course, Sharon.
57:30
SPEAKER_01: Oh, I my pleasure, guys.
57:32
SPEAKER_01: And if you want to follow me on LinkedIn, I put out a CX article every month, just like you guys do.
57:37
SPEAKER_01: So I'm I'm happy to happy to hold up with me on LinkedIn.
57:40
SPEAKER_02: I don't think LinkedIn and what else?
57:43
SPEAKER_02: What else do you want to do?
57:44
SPEAKER_01: LinkedIn, Twitter.
57:45
SPEAKER_01: Now, if you want to see my sarcastic stuff, you got to get me on Instagram.
57:48
SPEAKER_01: So Instagram, I put up Instagram, it's Jim underscore iub.
57:54
SPEAKER_01: Um, easy to find.
57:55
SPEAKER_01: If anybody looks at me, you could find me Jim Iub, I y O O B.
57:59
SPEAKER_01: There's not a lot of us out there, so I'm very easy to find on all social.
58:03
SPEAKER_01: Uh I I do I do Twitter, LinkedIn, Facebook.
58:06
SPEAKER_01: I was in Facebook jail a couple weeks ago, but I'm out now.
58:09
SPEAKER_02: So I will put that in there.
58:12
SPEAKER_02: Well, you have to understand why put why you got to put in nobody knows why they're in Facebook jail, right?
58:17
SPEAKER_01: But oh, I do, I do, and I will tell you why.
58:20
SPEAKER_01: I'll give you the story.
58:20
SPEAKER_01: So I'm talking about this next week.
58:22
SPEAKER_01: So I posted a meme of a kid putting a knife in a wall socket, and the meme said, in order to be successful, you had to be young and stupid.
58:34
SPEAKER_01: Facebook's Facebook's bot took it down and says, I'm spreading hate.
58:37
SPEAKER_01: I appealed the decision.
58:39
SPEAKER_01: I appealed the decision, and when I appealed the decision, they basically said, We appreciate your appeal, but I'm still denying you.
58:48
SPEAKER_01: So then I was in Facebook jail for two weeks.
58:51
SPEAKER_01: And just to just to piss them off, here's what I did.
58:55
SPEAKER_01: I posted a picture of a bird with a knife in his mouth, and that got through because obviously birds don't kill.
59:03
SPEAKER_02: Birds aren't real, you know that, right?
59:06
SPEAKER_02: Yes, that you know that meme?
59:08
SPEAKER_02: Anyway, guys, thanks again.
59:10
SPEAKER_02: This has been a lot of fun.
59:11
SPEAKER_02: We'll look forward to it another time.
59:13
SPEAKER_02: And and uh I'll be in touch because I have some project needs that I told you about.
59:17
SPEAKER_01: Absolutely.
59:17
SPEAKER_01: I'm looking forward to it.
59:19
SPEAKER_01: Man, it was great meeting you, Chris.
59:20
SPEAKER_01: Sharon, thanks for putting this together.
59:22
SPEAKER_01: Seth as always, man.
59:23
SPEAKER_01: Love our conversations.
59:24
SPEAKER_01: Have a blessed weekend, my friend.
59:29
SPEAKER_00: Thanks for listening to the Early AI Podcast with your hosts, Seth Early and Chris Featherstone.
59:35
SPEAKER_00: We hope you enjoyed the program and took away some ideas that you can start to implement in your organization right now.
59:42
SPEAKER_00: If you're stuck and need help, check out www.early.com for information about early information science consulting services.
59:50
SPEAKER_00: And of course, check the show notes for links to any resources we mentioned today.
59:54
SPEAKER_00: That's all for now.
59:55
SPEAKER_00: See you next time.